library(abind)
library(sciplot)
library(tidyverse)
library(ggplot2)
library(RColorBrewer)
library(plyr)
library(dplyr)
library(ggpubr)
library(patchwork)
getwd()
[1] "/Users/saradellwilliams/Dropbox/My_Publications/SpatEpiSCTLD/SCTLDepizootiology_lowerFLkeys/NonSpatialAnalyses"
my.data<-read.csv("SCTLD_END_exta.csv", header=T) #shortened file with just one column per date with levels of
my.data<-my.data[,-c(34:37)]
str(my.data)
'data.frame': 2012 obs. of 33 variables:
$ Site : int 1 1 1 1 1 1 1 1 1 1 ...
$ Plot : int 23 23 23 23 23 23 23 23 23 23 ...
$ Sps : chr "DLAB" "SINT" "SSID" "PPOR" ...
$ Max_width: int 11 22 25 11 18 11 11 16 10 8 ...
$ Coral_ID : chr "1_p23_t1_s0_c1_DLAB" "1_p23_t1_s0_c2_SINT" "1_p23_t1_s0_c3_SSID" "1_p23_t1_s0_c4_PPOR" ...
$ coords_x : num 0.448 0.789 0.834 0.226 0.463 ...
$ coords_y : num 0.236 0.479 0.628 1.927 2.225 ...
$ X5.1.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X6.1.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X6.21.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X7.16.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X8.17.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X10.30.18: chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X11.9.18 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X11.29.18: chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X12.13.18: chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X1.4.19 : chr "Healthy" "Healthy" "Healthy" "Healthy" ...
$ X1.18.19 :
Error in (function (srcref) : unimplemented type (29) in 'eval'
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
type 29 is unimplemented in 'type2char'Error in lapply(nf, function(n) eval(as.name(n))) :
INTEGER() can only be applied to a 'integer', not a 'unknown type #29'
Get the data into long format and get the timepoints into the right format for plotting via ggplot.
#move to a long format so that every row is now an observation of a colony at a single timepoint
data_long <- gather(my.data, key=timept, value=state, X5.1.18:X12.6.19,factor_key = TRUE)
head(data_long)
Site Plot Sps Max_width Coral_ID coords_x coords_y timept state
1 1 23 DLAB 11 1_p23_t1_s0_c1_DLAB 0.448071 0.2361312 X5.1.18 Healthy
2 1 23 SINT 22 1_p23_t1_s0_c2_SINT 0.789318 0.4790354 X5.1.18 Healthy
3 1 23 SSID 25 1_p23_t1_s0_c3_SSID 0.833828 0.6278998 X5.1.18 Healthy
4 1 23 PPOR 11 1_p23_t1_s0_c4_PPOR 0.225519 1.9267537 X5.1.18 Healthy
5 1 23 DSTO 18 1_p23_t1_s0_c5_DSTO 0.462908 2.2250339 X5.1.18 Healthy
6 1 23 CNAT 11 1_p23_t1_s0_c6_CNAT 0.151335 2.2052883 X5.1.18 Healthy
summary(as.factor(data_long$state))
Dead Healthy SCTLD Unknown
1530 49125 636 1021
#make the health states factors
data_long$state<-factor(data_long$state,levels=c("Healthy","SCTLD","Dead","Unknown"))
data_long$timept<-revalue(data_long$timept, c( "X5.1.18"="05-10-18","X6.1.18"="06-01-18","X6.21.18"="06-21-18","X7.16.18"="07-16-18","X8.17.18"="08-17-18","X10.30.18"="10-30-18", "X11.9.18"="11-09-18", "X11.29.18"="11-29-18","X12.13.18"="12-13-18","X1.4.19"="01-04-19","X1.18.19"="01-18-19","X2.8.19"="02-08-19","X3.4.19"="03-04-19","X3.21.19"="03-21-19","X4.11.19"="04-11-19","X5.2.19"="05-02-19","X5.16.19"="05-16-19","X5.28.19"="05-28-19","X6.13.19"="06-13-19","X7.1.19"="07-01-19","X7.22.19"="07-22-19","X8.16.19"="08-16-19","X9.17.19"="09-17-19","X10.14.19"="10-14-19","X11.12.19"="11-12-19","X12.6.19"="12-06-19"))
summary(data_long)
Site Plot Sps Max_width
Min. :1.000 Min. :23.00 Length:52312 Min. : 2.00
1st Qu.:1.000 1st Qu.:25.00 Class :character 1st Qu.: 11.00
Median :2.000 Median :28.00 Mode :character Median : 13.00
Mean :2.039 Mean :33.57 Mean : 22.79
3rd Qu.:3.000 3rd Qu.:45.00 3rd Qu.: 23.00
Max. :3.000 Max. :47.00 Max. :450.00
Coral_ID coords_x coords_y timept
Length:52312 Min. :-0.0729 Min. :-0.1202 05-10-18: 2012
Class :character 1st Qu.: 2.3136 1st Qu.: 2.2424 06-01-18: 2012
Mode :character Median : 4.7236 Median : 4.4857 06-21-18: 2012
Mean : 4.8322 Mean : 4.6890 07-16-18: 2012
3rd Qu.: 7.2089 3rd Qu.: 7.2046 08-17-18: 2012
Max. :10.0684 Max. :10.0440 10-30-18: 2012
NA's :416 NA's :416 (Other) :40240
state
Healthy:49125
SCTLD : 636
Dead : 1530
Unknown: 1021
#group by species, site, plot, timept and state
tryfreq<-data_long%>%
dplyr::group_by(Sps,Site,Plot,timept,state)%>%
dplyr::summarise(num=n())%>%
dplyr::group_by(Sps,Site,Plot,timept)%>%
dplyr::mutate(Fraction=num/sum(num))
`summarise()` regrouping output by 'Sps', 'Site', 'Plot', 'timept' (override with `.groups` argument)
#tryfreq
#expands the data so that all health state factors are listed for each timepoint for each species and joins it with old tp_counts... it also takes a long time.
tp_counts2<-tryfreq%>%
expand(state)%>%
left_join(tryfreq)
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Joining, by = c("Sps", "Site", "Plot", "timept", "state")
#previous step expands it by listing as NA, and now we make it so that NAs are 0s
tp_counts2[is.na(tp_counts2)] <- 0
#tp_counts2
fracs<-as.data.frame(tp_counts2)
colnames(fracs)[colnames(fracs)=="num"] <- "num_cols"
colnames(fracs)[colnames(fracs)=="Fraction"] <- "fraction"
summary(fracs)
Sps Site Plot timept state
Length:7696 Min. :1.000 Min. :23.00 05-10-18: 296 Healthy:1924
Class :character 1st Qu.:1.000 1st Qu.:25.00 06-01-18: 296 SCTLD :1924
Mode :character Median :2.000 Median :28.00 06-21-18: 296 Dead :1924
Mean :2.054 Mean :33.42 07-16-18: 296 Unknown:1924
3rd Qu.:3.000 3rd Qu.:45.00 08-17-18: 296
Max. :3.000 Max. :47.00 10-30-18: 296
(Other) :5920
num_cols fraction
Min. : 0.000 Min. :0.00
1st Qu.: 0.000 1st Qu.:0.00
Median : 0.000 Median :0.00
Mean : 6.797 Mean :0.25
3rd Qu.: 2.000 3rd Qu.:0.50
Max. :160.000 Max. :1.00
head(fracs)
Sps Site Plot timept state num_cols fraction
1 AAGA 2 28 05-10-18 Healthy 1 1
2 AAGA 2 28 05-10-18 SCTLD 0 0
3 AAGA 2 28 05-10-18 Dead 0 0
4 AAGA 2 28 05-10-18 Unknown 0 0
5 AAGA 2 28 06-01-18 Healthy 1 1
6 AAGA 2 28 06-01-18 SCTLD 0 0
We just really care about the species that showed disease signs
#filter out the not susceptible species
fracs_dis_sps<-fracs%>%
filter(Sps!="AAGA",Sps!="ACER",Sps!="ATEN",Sps!="EFAS",Sps!="MANG",Sps!="MMEA",Sps!="MYCE",Sps!="OCUL",Sps!="ODIF",Sps!="PAST",Sps!="PDIV",Sps!="PPOR",Sps!="SRAD")%>%
droplevels()
fracs_dis_sps$Sps<-as.factor(fracs_dis_sps$Sps)
str(fracs_dis_sps)
'data.frame': 5720 obs. of 7 variables:
$ Sps : Factor w/ 11 levels "CNAT","DLAB",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Site : int 1 1 1 1 1 1 1 1 1 1 ...
$ Plot : int 23 23 23 23 23 23 23 23 23 23 ...
$ timept : Factor w/ 26 levels "05-10-18","06-01-18",..: 1 1 1 1 2 2 2 2 3 3 ...
$ state : Factor w/ 4 levels "Healthy","SCTLD",..: 1 2 3 4 1 2 3 4 1 2 ...
$ num_cols: int 3 0 0 0 3 0 0 0 3 0 ...
$ fraction: num 1 0 0 0 1 0 0 0 1 0 ...
fracs_dis_sps$dates<-as.character(fracs_dis_sps$timept)
fracs_dis_sps$dates<-as.Date(fracs_dis_sps$dates,"%m-%d-%y")
site1<-fracs_dis_sps%>%
filter(Site==1,state!="Unknown")%>%
droplevels()
site1$state<-factor(site1$state,levels=c("Healthy","SCTLD","Dead"))#,"Unknown","TreatedUnknown","Treated"))
species_time<-ggplot(data=site1,mapping=aes(x=dates,y=fraction,color=state))
species_time+
geom_line(aes(color=state))+facet_grid(Sps~Plot)+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019",date_breaks = "2 months",date_labels = "%m %d %y")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","TreatedUnknown"="lightgoldenrod","Treated"="darkorange4","Unknown"="grey"),labels=c("Healthy","SCTLD","Dead","Unknown"),drop=FALSE)+
ggtitle("Mid-Channel")+
theme(axis.text.x = element_text(angle=70, hjust=1))
site2<-fracs_dis_sps%>%
filter(Site==2,state!="Unknown")%>%
droplevels()
site2$state<-factor(site2$state,levels=c("Healthy","SCTLD","Dead"))
ggplot(data=site2,mapping=aes(x=dates,y=fraction,color=state))+
geom_line(aes(color=state))+facet_grid(Sps~Plot)+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019",date_breaks = "2 months",date_labels = "%m %d %y")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","TreatedUnknown"="lightgoldenrod","Treated"="darkorange4","Unknown"="grey","TreatedSCTLD"="orange"),labels=c("Healthy","SCTLD","Dead","Unknown"),drop=FALSE)+
ggtitle("Off-Shore")+
theme(axis.text.x = element_text(angle=70, hjust=1))
site3<-fracs_dis_sps%>%
filter(Site==3,state!="Unknown")%>%
droplevels()
site3$state<-factor(site3$state,levels=c("Healthy","SCTLD","Dead"))
ggplot(data=site1,mapping=aes(x=dates,y=fraction,color=state))+
geom_line(aes(color=state))+facet_grid(Sps~Plot)+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019",date_breaks = "2 months",date_labels = "%m %d %y")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","TreatedUnknown"="lightgoldenrod","Treated"="darkorange4","Unknown"="grey","TreatedSCTLD"="orange"),labels=c("Healthy","SCTLD","Dead","Unknown"),drop=FALSE)+
ggtitle("Nearshore")+
theme(axis.text.x = element_text(angle=70, hjust=1))
With only species that got disease
#group by species, site, plot, timept and state
data_long_dis<-data_long%>%
filter(Sps!="AAGA",Sps!="ACER",Sps!="ATEN",Sps!="EFAS",Sps!="MANG",Sps!="MMEA",Sps!="MYCE",Sps!="OCUL",Sps!="ODIF",Sps!="PAST",Sps!="PDIV",Sps!="PPOR",Sps!="SRAD")%>%
droplevels()
tryfreq_plot_dis<-data_long_dis%>%
dplyr::group_by(Site,Plot,timept,state)%>%
dplyr::summarise(num=n())%>%
dplyr::group_by(Site,Plot,timept)%>%
dplyr::mutate(Fraction=num/sum(num))
`summarise()` regrouping output by 'Site', 'Plot', 'timept' (override with `.groups` argument)
tryfreq_plot_dis
# A tibble: 466 x 6
# Groups: Site, Plot, timept [156]
Site Plot timept state num Fraction
<int> <int> <fct> <fct> <int> <dbl>
1 1 23 05-10-18 Healthy 211 1
2 1 23 06-01-18 Healthy 211 1
3 1 23 06-21-18 Healthy 211 1
4 1 23 07-16-18 Healthy 211 1
5 1 23 08-17-18 Healthy 210 0.995
6 1 23 08-17-18 Unknown 1 0.00474
7 1 23 10-30-18 Healthy 210 0.995
8 1 23 10-30-18 Dead 1 0.00474
9 1 23 11-09-18 Healthy 209 0.991
10 1 23 11-09-18 Dead 1 0.00474
# … with 456 more rows
#expands the data so that all health state factors are listed for each timepoint for each species and joins it with old tp_counts... it also takes a long time.
tp_counts2_plot_dis<-tryfreq_plot_dis%>%
expand(state)%>%
left_join(tryfreq_plot_dis)
Joining, by = c("Site", "Plot", "timept", "state")
#previous step expands it by listing as NA, and now we make it so that NAs are 0s
tp_counts2_plot_dis[is.na(tp_counts2_plot_dis)] <- 0
tp_counts2_plot_dis
# A tibble: 624 x 6
# Groups: Site, Plot, timept [156]
Site Plot timept state num Fraction
<int> <int> <fct> <fct> <int> <dbl>
1 1 23 05-10-18 Healthy 211 1
2 1 23 05-10-18 SCTLD 0 0
3 1 23 05-10-18 Dead 0 0
4 1 23 05-10-18 Unknown 0 0
5 1 23 06-01-18 Healthy 211 1
6 1 23 06-01-18 SCTLD 0 0
7 1 23 06-01-18 Dead 0 0
8 1 23 06-01-18 Unknown 0 0
9 1 23 06-21-18 Healthy 211 1
10 1 23 06-21-18 SCTLD 0 0
# … with 614 more rows
fracs_plot_dis<-as.data.frame(tp_counts2_plot_dis)
colnames(fracs_plot_dis)[colnames(fracs_plot_dis)=="num"] <- "num_cols"
colnames(fracs_plot_dis)[colnames(fracs_plot_dis)=="Fraction"] <- "fraction"
summary(fracs_plot_dis)
Site Plot timept state num_cols
Min. :1 Min. :23.0 05-10-18: 24 Healthy:156 Min. : 0.00
1st Qu.:1 1st Qu.:25.0 06-01-18: 24 SCTLD :156 1st Qu.: 0.00
Median :2 Median :27.5 06-21-18: 24 Dead :156 Median : 6.00
Mean :2 Mean :32.5 07-16-18: 24 Unknown:156 Mean : 63.83
3rd Qu.:3 3rd Qu.:45.0 08-17-18: 24 3rd Qu.: 75.25
Max. :3 Max. :47.0 10-30-18: 24 Max. :383.00
(Other) :480
fraction
Min. :0.0000
1st Qu.:0.0000
Median :0.0250
Mean :0.2500
3rd Qu.:0.3167
Max. :1.0000
head(fracs_plot_dis)
Site Plot timept state num_cols fraction
1 1 23 05-10-18 Healthy 211 1
2 1 23 05-10-18 SCTLD 0 0
3 1 23 05-10-18 Dead 0 0
4 1 23 05-10-18 Unknown 0 0
5 1 23 06-01-18 Healthy 211 1
6 1 23 06-01-18 SCTLD 0 0
fracs_plot_dis$dates<-as.character(fracs_plot_dis$timept)
fracs_plot_dis$dates<-as.Date(fracs_plot_dis$dates,"%m-%d-%y")
sitelabels<-c('1'="Mid-Channel",'2'="Offshore",'3'="Nearshore")
site1<-fracs_plot_dis%>%
filter(Site==1)
species_time<-ggplot(data=site1,mapping=aes(x=dates,y=fraction,color=state))
site1plot<-species_time+
geom_line(aes(color=state))+facet_grid(Plot~Site,labeller=labeller(Site = sitelabels))+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous(" ",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=FALSE)
site2<-fracs_plot_dis%>%
filter(Site==2)
species_time<-ggplot(data=site2,mapping=aes(x=dates,y=fraction,color=state))
site2plot<-species_time+
geom_line(aes(color=state))+facet_grid(Plot~Site,labeller=labeller(Site = sitelabels))+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous(" ",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=FALSE)
site3<-fracs_plot_dis%>%
filter(Site==3)
species_time<-ggplot(data=site3,mapping=aes(x=dates,y=fraction,color=state))
site3plot<-species_time+
geom_line(aes(color=state))+facet_grid(Plot~Site,labeller=labeller(Site = sitelabels))+
geom_point(aes(color=state))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies with SCTLD",breaks=c(0,0.2,0.4,0.6,0.8,1))+
scale_x_date("Survey Dates 2018-2019")+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=FALSE)
ggarrange(site3plot,site1plot,site2plot, ncol=3,common.legend=TRUE)
site.means<-aggregate(fraction~state+dates+Site,FUN=mean,data=fracs_plot_dis)
site.se<-aggregate(fraction~state+dates+Site,FUN=function(x) sd(x)/sqrt(length(x)),data=fracs_plot_dis)
#site.se
stats<-cbind(site.means,stander=site.se$fraction)
#sitelabels
stats$Site<-as.factor(stats$Site)
levels(stats$Site)
[1] "1" "2" "3"
stats$Site <- factor(stats$Site,levels(stats$Site)[c(3,1,2)])
#just look at diseased and dead
stats_filtered<-stats%>%
filter(state!="Unknown",state!="Healthy")
ggplot(data=stats_filtered,mapping=aes(x=dates,y=fraction,color=state))+
geom_line(aes(color=state))+facet_wrap(~Site,nrow=3,labeller=labeller(Site = sitelabels))+
geom_point(aes(color=state))+geom_pointrange(aes(color=state,ymin=fraction-1.96*stander, ymax=fraction+1.96*stander))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies with SCTLD",c(0,0.2),breaks=c(0,0.05,0.1,0.15,0.2))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black"),drop=TRUE)+
#ggtitle("Disease Prevalence (all surveyed corals)")+
theme(axis.text.x = element_text(angle=70, hjust=1))+scale_x_date("Survey Dates 2018-2019", breaks=stats$dates,date_labels="%b %d %y")+theme(legend.position = "right")
#sites (averaged plots) are replicates
all.means<-aggregate(fraction~state+dates,FUN=mean,data=site.means)
all.se<-aggregate(fraction~state+dates,FUN=function(x) sd(x)/sqrt(length(x)),data=site.means)
all.stats<-cbind(all.means,stander=all.se$fraction)
#all.stats
#all_filtered<-all.stats%>%
# filter(state!="Unknown")
### you have to play with this bit to get it in the right order
#stats_filtered$Site <- factor(stats_filtered$Site,levels(stats_filtered$Site)[c(3,2,1)])
#levels(stats_filtered$Site)
#sitelabels
species_time<-ggplot(data=all.stats,mapping=aes(x=dates,y=fraction,color=state))
species_time+
geom_line(aes(color=state))+
geom_point(aes(color=state))+geom_pointrange(aes(color=state,ymin=fraction-1.96*stander, ymax=fraction+1.96*stander))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of Colonies with SCTLD",c(0.1,1),breaks=c(0.1,0.2,0.4,0.6,0.8,1))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
#ggtitle("Disease Prevalence (all surveyed corals)")+
theme(axis.text.x = element_text(angle=70, hjust=1))+scale_x_date("Survey Dates 2018-2019", breaks=all.stats$dates,date_labels="%b %d %y")+theme(legend.position = "bottom")
all_filtered<-all.stats%>%
filter(state!="Healthy",state!="Unknown")
species_time<-ggplot(data=all_filtered,mapping=aes(x=dates,y=fraction,color=state))
species_time+
geom_line(aes(color=state))+
geom_point(aes(color=state))+geom_pointrange(aes(color=state,ymin=fraction-stander, ymax=fraction+stander))+
theme_bw()+
theme(panel.grid.minor =element_blank())+
scale_y_continuous("Fraction of colonies",c(0,0.2),breaks=c(0,0.05,0.1,0.15,0.2))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
#ggtitle("Disease Prevalence (all surveyed corals)")+
theme(axis.text.x = element_text(angle=70, hjust=1))+scale_x_date("Survey Dates 2018-2019", breaks=all.stats$dates,date_labels="%b %d %y")+theme(legend.position = "bottom")
need the new_inf_corals function to determine the newly diseased corals
new_inf_corals<-function(df,steps,x){
## newly_I
newly_I<-matrix(0, nrow = nrow(df), ncol = steps) #blank matrix for storing newly infected corals
#newly_I
for (i in 1:steps){ #for each survey time point
col<-x+i #start with first tp after init tp
prev<-x+i-1
for (j in 1:(nrow(df))){ #for each row in the df
#print (df[j,col])
if ( df[j,col]=="SCTLD" ){ #if it's disease
#print( "found one")
if (df[j,prev]!="SCTLD"){ # and if it wasnt diseased before
newly_I[j,i]<-1 #add it to newly infected
}
}
}
}
return(newly_I)
}
#need just suscpetible species
dis.sps<-my.data%>%
filter(Sps!="AAGA",Sps!="ACER",Sps!="ATEN",Sps!="EFAS",Sps!="MANG",Sps!="MMEA",Sps!="MYCE",Sps!="OCUL",Sps!="ODIF",Sps!="PAST",Sps!="PDIV",Sps!="PPOR",Sps!="SRAD")%>%
droplevels()
#ok, shorten dis.sps dataset to just 10/30/19-12/6/19, becuase didnt see disease until then
## this is the wide format dataset
#colnames(dis.sps)
my.datas<-dis.sps%>%
dplyr::select(Site,Plot,Sps,coords_x,coords_y,X8.17.18,X10.30.18,X11.9.18,X11.29.18,X12.13.18,X1.4.19,X1.18.19,X2.8.19,X3.4.19,X3.21.19,X4.11.19,X5.2.19,X5.16.19,X5.28.19,X6.13.19,X7.1.19,X7.22.19,X8.16.19,X9.17.19,X10.14.19,X11.12.19,X12.6.19)
colnames(dis.sps)
[1] "Site" "Plot" "Sps" "Max_width" "Coral_ID" "coords_x"
[7] "coords_y" "X5.1.18" "X6.1.18" "X6.21.18" "X7.16.18" "X8.17.18"
[13] "X10.30.18" "X11.9.18" "X11.29.18" "X12.13.18" "X1.4.19" "X1.18.19"
[19] "X2.8.19" "X3.4.19" "X3.21.19" "X4.11.19" "X5.2.19" "X5.16.19"
[25] "X5.28.19" "X6.13.19" "X7.1.19" "X7.22.19" "X8.16.19" "X9.17.19"
[31] "X10.14.19" "X11.12.19" "X12.6.19"
#refactor data to all have same levels
my.datas$X8.17.18<-factor(my.datas$X8.17.18,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X10.30.18<-factor(my.datas$X10.30.18,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X11.9.18<-factor(my.datas$X11.9.18,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X11.29.18<-factor(my.datas$X11.29.18,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X12.13.18<-factor(my.datas$X12.13.18,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X1.4.19<-factor(my.datas$X1.4.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X1.18.19<-factor(my.datas$X1.18.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X2.8.19<-factor(my.datas$X2.8.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X3.4.19<-factor(my.datas$X3.4.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X3.21.19<-factor(my.datas$X3.21.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X4.11.19<-factor(my.datas$X4.11.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X5.2.19<-factor(my.datas$X5.2.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X5.16.19<-factor(my.datas$X5.16.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X5.28.19<-factor(my.datas$X5.28.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X6.13.19<-factor(my.datas$X6.13.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X7.1.19<-factor(my.datas$X7.1.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X7.22.19<-factor(my.datas$X7.22.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X8.16.19<-factor(my.datas$X8.16.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X9.17.19<-factor(my.datas$X9.17.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X10.14.19<-factor(my.datas$X10.14.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X11.12.19<-factor(my.datas$X11.12.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
my.datas$X12.6.19<-factor(my.datas$X12.6.19,levels=c("Healthy","SCTLD","Dead","Unknown"))
head(my.datas)
Site Plot Sps coords_x coords_y X8.17.18 X10.30.18 X11.9.18 X11.29.18 X12.13.18
1 1 23 DLAB 0.448071 0.2361312 Healthy Healthy Healthy Healthy Healthy
2 1 23 SINT 0.789318 0.4790354 Healthy Healthy Healthy Healthy Healthy
3 1 23 SSID 0.833828 0.6278998 Healthy Healthy Healthy Healthy Healthy
4 1 23 DSTO 0.462908 2.2250339 Healthy Healthy Healthy Unknown Healthy
5 1 23 CNAT 0.151335 2.2052883 Healthy Dead Dead Dead Dead
6 1 23 SINT 0.314540 5.9977166 Healthy Healthy Healthy Healthy Healthy
X1.4.19 X1.18.19 X2.8.19 X3.4.19 X3.21.19 X4.11.19 X5.2.19 X5.16.19 X5.28.19
1 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
2 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
3 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
4 SCTLD SCTLD SCTLD SCTLD SCTLD SCTLD SCTLD SCTLD Dead
5 Dead Dead Dead Dead Dead Dead Dead Dead Dead
6 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
X6.13.19 X7.1.19 X7.22.19 X8.16.19 X9.17.19 X10.14.19 X11.12.19 X12.6.19
1 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
2 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
3 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
4 Dead Dead Dead Dead Dead Dead Dead Dead
5 Dead Dead Dead Dead Dead Dead Dead Dead
6 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
#Split into plots for analysis
s3.p45<-my.datas%>%
filter(Plot==45)
s3.p47<-my.datas%>%
filter(Plot==47)
s1.p23<-my.datas%>%
filter(Plot==23)
s1.p25<-my.datas%>%
filter(Plot==25)
s2.p27<-my.datas%>%
filter(Plot==27)
s2.p28<-my.datas%>%
filter(Plot==28)
plotnum<-s3.p45
s3.p45
Site Plot Sps coords_x coords_y X8.17.18 X10.30.18 X11.9.18 X11.29.18
1 3 45 SSID 0.426747 1.4329768 Healthy Healthy Healthy Healthy
2 3 45 SBOU 0.936817 2.1973145 Healthy Healthy Healthy Healthy
3 3 45 SBOU 0.423617 2.1276741 Healthy Healthy Healthy Healthy
4 3 45 SSID 0.366295 2.8532658 Healthy Healthy Healthy Healthy
5 3 45 SINT 0.337989 3.1371188 Healthy Healthy Healthy Healthy
6 3 45 SSID 0.336282 3.5160446 Healthy Healthy Healthy Healthy
7 3 45 SINT 0.574249 4.6874716 Healthy Healthy Healthy Healthy
8 3 45 SBOU 0.384359 4.8429677 Healthy Healthy Healthy Healthy
9 3 45 SSID 0.867547 5.5753869 Healthy Healthy Healthy Healthy
10 3 45 PSTR 0.352498 5.9162494 Healthy Healthy Healthy Healthy
11 3 45 SINT 0.513939 6.0761834 Healthy Healthy Healthy Healthy
12 3 45 PSTR 0.375967 6.7060196 Healthy Healthy Healthy Healthy
13 3 45 SINT 0.835827 6.6170915 Healthy Healthy Healthy Healthy
14 3 45 DSTO 0.969959 6.8398384 Healthy Healthy Healthy Healthy
15 3 45 CNAT 0.374403 7.0533682 Healthy Healthy Healthy Healthy
16 3 45 CNAT 1.076354 7.2201297 Healthy Healthy Healthy Healthy
17 3 45 SINT 1.127418 7.8839326 Healthy Healthy Healthy Healthy
18 3 45 SINT 0.938097 7.9131202 Healthy Healthy Healthy Healthy
19 3 45 SINT 1.044635 8.2618343 Healthy Healthy Healthy Healthy
20 3 45 SSID 0.907943 8.6074761 Healthy Healthy Healthy Healthy
21 3 45 CNAT 1.675467 0.2171711 Healthy Healthy Healthy Healthy
22 3 45 PSTR 1.400233 1.3189577 Healthy Healthy Healthy Healthy
23 3 45 SINT 1.671057 1.1960628 Healthy Healthy Healthy Healthy
24 3 45 SINT 1.507197 1.5729404 Healthy Healthy Healthy Healthy
25 3 45 SSID 2.019259 1.8951980 Healthy Healthy Healthy Healthy
26 3 45 SINT 1.640760 1.9219959 Healthy Healthy Healthy Healthy
27 3 45 SSID 1.393548 2.8030838 Healthy Healthy Healthy Healthy
28 3 45 SINT 1.581304 3.1212449 Healthy Healthy Healthy Healthy
29 3 45 MCAV 1.741039 3.6601047 Healthy Healthy Healthy Healthy
30 3 45 MCAV 1.764935 4.3551434 Healthy Healthy Healthy Healthy
31 3 45 SINT 1.330678 4.7601843 Healthy Healthy Healthy Healthy
32 3 45 SINT 2.010156 3.9161356 Healthy Healthy Healthy Healthy
33 3 45 SBOU 1.702350 6.2490897 Healthy Healthy Healthy Healthy
34 3 45 SINT 1.423987 8.0455735 Healthy Healthy Healthy Healthy
35 3 45 MCAV 1.910162 8.1148726 Healthy Healthy Healthy Healthy
36 3 45 SINT 1.988251 8.7790168 Healthy Healthy Healthy Healthy
37 3 45 SINT 1.824391 9.1558944 Healthy Healthy Healthy Healthy
X12.13.18 X1.4.19 X1.18.19 X2.8.19 X3.4.19 X3.21.19 X4.11.19 X5.2.19 X5.16.19
1 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
2 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
3 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
4 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
5 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
6 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
7 Healthy Healthy Healthy Unknown Unknown Unknown Unknown Healthy Healthy
8 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Unknown
9 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
10 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
11 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
12 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
13 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
14 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
15 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy SCTLD
16 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy SCTLD
17 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
18 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
19 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
20 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
21 Healthy Healthy Healthy Healthy Healthy Healthy SCTLD Dead Dead
22 Healthy Healthy Healthy Healthy Healthy Healthy SCTLD SCTLD SCTLD
23 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
24 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
25 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
26 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
27 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
28 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
29 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
30 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
31 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
32 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
33 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
34 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
35 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
36 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
37 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
X5.28.19 X6.13.19 X7.1.19 X7.22.19 X8.16.19 X9.17.19 X10.14.19 X11.12.19
1 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
2 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
3 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
4 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
5 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
6 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
7 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
8 Unknown Unknown Healthy Healthy Healthy Healthy Healthy Unknown
9 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
10 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
11 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
12 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
13 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
14 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
15 Dead Dead Dead Dead Dead Dead Dead Dead
16 SCTLD Dead Dead Dead Dead Dead Dead Dead
17 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
18 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
19 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
20 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
21 Dead Dead Dead Dead Dead Dead Dead Dead
22 Dead Dead Dead Dead Dead Dead Dead Dead
23 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
24 Healthy Healthy Healthy Healthy Healthy Healthy Unknown Unknown
25 Healthy Healthy Healthy Healthy Healthy Healthy Healthy SCTLD
26 Healthy Healthy Healthy Healthy Healthy Healthy Unknown Unknown
27 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
28 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
29 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
30 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
31 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
32 Healthy Healthy Healthy Healthy Healthy Healthy Unknown Unknown
33 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
34 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
35 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
36 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
37 Healthy Healthy Healthy Healthy Healthy Healthy Healthy Healthy
X12.6.19
1 Healthy
2 Healthy
3 Healthy
4 Healthy
5 Healthy
6 Healthy
7 Healthy
8 Unknown
9 Healthy
10 Healthy
11 Healthy
12 Healthy
13 Healthy
14 Healthy
15 Dead
16 Dead
17 Healthy
18 Healthy
19 Healthy
20 Healthy
21 Dead
22 Dead
23 Healthy
24 Unknown
25 SCTLD
26 Unknown
27 Healthy
28 Healthy
29 Healthy
30 Healthy
31 Healthy
32 Unknown
33 Healthy
34 Healthy
35 Healthy
36 Healthy
37 Healthy
[ reached 'max' / getOption("max.print") -- omitted 203 rows ]
x<-6 #start date
steps<-ncol(plotnum)-x
steps
[1] 21
p45.newinf<-new_inf_corals(plotnum,steps,x)
colSums(p45.newinf)
[1] 0 0 0 0 0 0 0 4 3 11 7 10 2 6 0 0 0 1 1 6 2
#length(colSums(p45.newinf))
p45.incidence<-cbind(colnames(s3.p45)[7:ncol(plotnum)], (colSums(p45.newinf)))
p45.incidence<-data.frame(p45.incidence)
colnames(p45.incidence)<-c("date","newI")
p45.incidence
date newI
1 X10.30.18 0
2 X11.9.18 0
3 X11.29.18 0
4 X12.13.18 0
5 X1.4.19 0
6 X1.18.19 0
7 X2.8.19 0
8 X3.4.19 4
9 X3.21.19 3
10 X4.11.19 11
11 X5.2.19 7
12 X5.16.19 10
13 X5.28.19 2
14 X6.13.19 6
15 X7.1.19 0
16 X7.22.19 0
17 X8.16.19 0
18 X9.17.19 1
19 X10.14.19 1
20 X11.12.19 6
21 X12.6.19 2
#now plot 47
plotnum<-s3.p47
x<-6 #start date
steps<-ncol(plotnum)-x
p47.newinf<-new_inf_corals(plotnum,steps,x)
#p47.newinf
#length(colSums(p47.newinf))
p47.incidence<-cbind(colnames(s3.p47)[7:ncol(plotnum)],(colSums(p47.newinf)))
p47.incidence<-data.frame(p47.incidence)
colnames(p47.incidence)<-c("date","newI")
#p47.incidence
plotnum<-s1.p23
x<-6 #start date
steps<-ncol(plotnum)-x
steps
[1] 21
p23.newinf<-new_inf_corals(plotnum,steps,x)
p23.newinf
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 1 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 1 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[24,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[25,] 0 0 0 0 0 0 0 1 0 0 0 0 0 0
[26,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[27,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[28,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[30,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[31,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[32,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[33,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[34,] 0 0 0 0 0 0 0 0 0 0 1 0 0 0
[35,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[36,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[37,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[39,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[40,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[41,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[42,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[43,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[44,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[45,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[46,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[47,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0
[23,] 0 0 0 0 0 0 0
[24,] 0 0 0 0 0 0 0
[25,] 0 0 0 0 0 0 0
[26,] 0 0 0 0 0 0 0
[27,] 0 0 0 0 0 0 0
[28,] 0 0 0 0 0 0 0
[29,] 0 0 0 0 0 0 0
[30,] 0 0 0 0 0 0 0
[31,] 0 0 0 0 0 0 0
[32,] 0 0 0 0 0 0 0
[33,] 0 0 0 0 0 0 0
[34,] 0 0 0 0 0 0 0
[35,] 0 0 0 0 0 0 0
[36,] 0 0 0 0 0 0 0
[37,] 0 0 0 0 0 0 0
[38,] 0 0 0 0 0 0 0
[39,] 0 0 0 0 0 0 0
[40,] 0 0 0 0 0 0 0
[41,] 0 0 0 0 0 0 0
[42,] 0 0 0 0 0 0 0
[43,] 0 0 0 0 0 0 0
[44,] 0 0 0 0 0 0 0
[45,] 0 0 0 0 0 0 0
[46,] 0 0 0 0 0 0 0
[47,] 0 0 0 0 0 0 0
[ reached getOption("max.print") -- omitted 164 rows ]
#length(colSums(p45.newinf))
p23.incidence<-cbind(colnames(s1.p23)[7:ncol(plotnum)], (colSums(p23.newinf)))
p23.incidence<-data.frame(p23.incidence)
colnames(p23.incidence)<-c("date","newI")
p23.incidence
date newI
1 X10.30.18 0
2 X11.9.18 0
3 X11.29.18 0
4 X12.13.18 0
5 X1.4.19 4
6 X1.18.19 0
7 X2.8.19 1
8 X3.4.19 2
9 X3.21.19 0
10 X4.11.19 1
11 X5.2.19 1
12 X5.16.19 1
13 X5.28.19 1
14 X6.13.19 1
15 X7.1.19 0
16 X7.22.19 0
17 X8.16.19 0
18 X9.17.19 0
19 X10.14.19 0
20 X11.12.19 0
21 X12.6.19 1
#now plot 25
plotnum<-s1.p25
x<-6 #start date
steps<-ncol(plotnum)-x
p25.newinf<-new_inf_corals(plotnum,steps,x)
#p47.newinf
#length(colSums(p47.newinf))
p25.incidence<-cbind(colnames(s1.p25)[7:ncol(plotnum)],(colSums(p25.newinf)))
p25.incidence<-data.frame(p25.incidence)
colnames(p25.incidence)<-c("date","newI")
#p47.incidence
plotnum<-s2.p27
x<-6 #start date
steps<-ncol(plotnum)-x
steps
[1] 21
p27.newinf<-new_inf_corals(plotnum,steps,x)
p27.newinf
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[23,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[24,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[25,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[26,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[27,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[28,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[29,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[30,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[31,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[32,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[33,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[34,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[35,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[36,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[37,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[38,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[39,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[40,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[41,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[42,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[43,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[44,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[45,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[46,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[47,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[,15] [,16] [,17] [,18] [,19] [,20] [,21]
[1,] 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 0
[13,] 0 0 0 0 0 0 0
[14,] 0 0 0 0 0 0 0
[15,] 0 0 0 0 0 0 0
[16,] 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0
[23,] 0 0 0 0 0 0 0
[24,] 0 0 0 0 0 0 0
[25,] 0 0 0 0 0 0 0
[26,] 0 0 0 0 0 0 0
[27,] 0 0 0 0 0 0 0
[28,] 0 0 0 0 0 0 0
[29,] 0 0 0 0 0 0 0
[30,] 0 0 0 0 0 0 0
[31,] 0 0 0 0 0 0 0
[32,] 0 0 0 0 0 0 0
[33,] 0 0 0 0 0 0 0
[34,] 0 0 0 0 0 0 0
[35,] 0 0 0 0 0 0 0
[36,] 0 0 0 0 0 0 0
[37,] 0 1 0 0 0 0 0
[38,] 0 0 0 0 0 0 0
[39,] 0 0 0 0 0 0 0
[40,] 0 0 0 0 0 0 0
[41,] 0 0 0 0 0 0 0
[42,] 0 0 0 0 0 0 0
[43,] 0 0 0 0 0 0 0
[44,] 0 0 0 0 0 0 0
[45,] 0 0 0 0 0 0 0
[46,] 0 0 0 0 0 0 0
[47,] 0 0 0 0 0 0 0
[ reached getOption("max.print") -- omitted 142 rows ]
#length(colSums(p45.newinf))
p27.incidence<-cbind(colnames(s2.p27)[7:ncol(plotnum)], (colSums(p27.newinf)))
p27.incidence<-data.frame(p27.incidence)
colnames(p27.incidence)<-c("date","newI")
p27.incidence
date newI
1 X10.30.18 1
2 X11.9.18 0
3 X11.29.18 0
4 X12.13.18 2
5 X1.4.19 1
6 X1.18.19 2
7 X2.8.19 0
8 X3.4.19 0
9 X3.21.19 0
10 X4.11.19 0
11 X5.2.19 0
12 X5.16.19 0
13 X5.28.19 0
14 X6.13.19 0
15 X7.1.19 0
16 X7.22.19 1
17 X8.16.19 0
18 X9.17.19 0
19 X10.14.19 1
20 X11.12.19 0
21 X12.6.19 0
#now plot 28
plotnum<-s2.p28
x<-6 #start date
steps<-ncol(plotnum)-x
p28.newinf<-new_inf_corals(plotnum,steps,x)
#p28.newinf
#length(colSums(p47.newinf))
p28.incidence<-cbind(colnames(s2.p28)[7:ncol(plotnum)],(colSums(p28.newinf)))
p28.incidence<-data.frame(p28.incidence)
colnames(p28.incidence)<-c("date","newI")
#p47.incidence
plots.incidence<-cbind(p45.incidence,p47.incidence[2],p23.incidence[2],p25.incidence[2],p27.incidence[2],p28.incidence[2])
colnames(plots.incidence)<-c("date","plot45","plot47","plot23","plot25","plot27","plot28")
plots.incidence$date<-revalue(plots.incidence$date, c("X10.30.18" ="10-30-18","X11.9.18"="11-09-18","X11.29.18"="11-29-18", "X12.13.18"="12-13-18" ,"X1.4.19"="01-04-19" , "X1.18.19" ="01-18-19","X2.8.19"="02-08-19", "X3.4.19"="03-04-19","X3.21.19"="03-21-19","X4.11.19"= "04-11-19","X5.2.19"="05-02-19","X5.16.19"="05-16-19","X5.28.19"="05-28-19","X6.13.19"="06-13-19","X7.1.19"="07-01-19","X7.22.19"="07-22-19","X8.16.19"="08-16-19","X9.17.19"="09-17-19","X10.14.19"="10-14-19","X11.12.19"="11-12-19","X12.6.19"="12-06-19"))
plots.incidence$date<-as.character(plots.incidence$date)
plots.incidence$date<-as.Date(plots.incidence$date,"%m-%d-%y")
plots.incidence
date plot45 plot47 plot23 plot25 plot27 plot28
1 2018-10-30 0 0 0 0 1 0
2 2018-11-09 0 0 0 0 0 1
3 2018-11-29 0 0 0 1 0 1
4 2018-12-13 0 0 0 0 2 0
5 2019-01-04 0 0 4 0 1 3
6 2019-01-18 0 0 0 1 2 4
7 2019-02-08 0 2 1 0 0 1
8 2019-03-04 4 9 2 3 0 1
9 2019-03-21 3 7 0 3 0 1
10 2019-04-11 11 18 1 2 0 0
11 2019-05-02 7 9 1 2 0 0
12 2019-05-16 10 15 1 1 0 0
13 2019-05-28 2 3 1 0 0 2
14 2019-06-13 6 0 1 0 0 0
15 2019-07-01 0 2 0 1 0 0
16 2019-07-22 0 0 0 0 1 0
17 2019-08-16 0 1 0 0 0 0
18 2019-09-17 1 0 0 0 0 0
19 2019-10-14 1 1 0 0 1 0
20 2019-11-12 6 5 0 0 0 1
21 2019-12-06 2 5 1 1 0 0
plots.incidence$plot45<-as.numeric(plots.incidence$plot45)
plots.incidence$plot47<-as.numeric(plots.incidence$plot47)
plots.incidence$plot23<-as.numeric(plots.incidence$plot23)
plots.incidence$plot25<-as.numeric(plots.incidence$plot25)
plots.incidence$plot27<-as.numeric(plots.incidence$plot27)
plots.incidence$plot28<-as.numeric(plots.incidence$plot28)
incidence_long <- gather(plots.incidence, key=plot, value=newI, plot45:plot28,factor_key = TRUE)
incidence_long$newI<-as.numeric(incidence_long$newI)
plots.incidence
date plot45 plot47 plot23 plot25 plot27 plot28
1 2018-10-30 0 0 0 0 1 0
2 2018-11-09 0 0 0 0 0 1
3 2018-11-29 0 0 0 1 0 1
4 2018-12-13 0 0 0 0 2 0
5 2019-01-04 0 0 4 0 1 3
6 2019-01-18 0 0 0 1 2 4
7 2019-02-08 0 2 1 0 0 1
8 2019-03-04 4 9 2 3 0 1
9 2019-03-21 3 7 0 3 0 1
10 2019-04-11 11 18 1 2 0 0
11 2019-05-02 7 9 1 2 0 0
12 2019-05-16 10 15 1 1 0 0
13 2019-05-28 2 3 1 0 0 2
14 2019-06-13 6 0 1 0 0 0
15 2019-07-01 0 2 0 1 0 0
16 2019-07-22 0 0 0 0 1 0
17 2019-08-16 0 1 0 0 0 0
18 2019-09-17 1 0 0 0 0 0
19 2019-10-14 1 1 0 0 1 0
20 2019-11-12 6 5 0 0 0 1
21 2019-12-06 2 5 1 1 0 0
str(incidence_long)
'data.frame': 126 obs. of 3 variables:
$ date: Date, format: "2018-10-30" "2018-11-09" ...
$ plot: Factor w/ 6 levels "plot45","plot47",..: 1 1 1 1 1 1 1 1 1 1 ...
$ newI: num 0 0 0 0 0 0 0 4 3 11 ...
ggplot(incidence_long, aes(x=date, y=newI, col=plot)) +
geom_path()+theme(panel.background = element_blank())+ theme(text = element_text(family = "Times New Roman"),axis.text.x = element_text(angle = 70, hjust = 1))+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+labs(x="Survey Dates",y="Disease Incidence")+scale_x_date(date_breaks = "2 week", date_minor_breaks = "1 week",date_labels="%b %d")+geom_point()+scale_y_continuous(breaks=seq(0,18,2),expand = c(0, 0),limits=c(0,18.5))
tot.inc<-rowSums(plots.incidence[,2:7])
tot.inc
[1] 1 1 2 2 8 7 4 19 14 32 19 27 8 7 3 1 1 1 3 12 9
plots.incidence$date
[1] "2018-10-30" "2018-11-09" "2018-11-29" "2018-12-13" "2019-01-04" "2019-01-18"
[7] "2019-02-08" "2019-03-04" "2019-03-21" "2019-04-11" "2019-05-02" "2019-05-16"
[13] "2019-05-28" "2019-06-13" "2019-07-01" "2019-07-22" "2019-08-16" "2019-09-17"
[19] "2019-10-14" "2019-11-12" "2019-12-06"
all.incidence<-data.frame("Date"=plots.incidence$date,"newInc"=tot.inc)
all.incidence
Date newInc
1 2018-10-30 1
2 2018-11-09 1
3 2018-11-29 2
4 2018-12-13 2
5 2019-01-04 8
6 2019-01-18 7
7 2019-02-08 4
8 2019-03-04 19
9 2019-03-21 14
10 2019-04-11 32
11 2019-05-02 19
12 2019-05-16 27
13 2019-05-28 8
14 2019-06-13 7
15 2019-07-01 3
16 2019-07-22 1
17 2019-08-16 1
18 2019-09-17 1
19 2019-10-14 3
20 2019-11-12 12
21 2019-12-06 9
all.incidence$newInc.scaled<-all.incidence$newInc/300
#### all data
ggplot()+
geom_line(data=all_filtered,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=all_filtered,mapping=aes(x=dates,y=fraction,color=state))+geom_pointrange(data=all_filtered,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
geom_bar(data=all.incidence,aes(x=Date,y=newInc.scaled),stat="identity",fill="light grey")+
scale_y_continuous(name = "Fraction of colonies", breaks=seq(0,.2,.01),expand = c(0, 0),limits=c(0,.2),sec.axis = sec_axis(~.*300,name ="Disease incidence",breaks=c(0,5,10,15,20,25,30,35)))+
geom_line(data=all_filtered,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=all_filtered,mapping=aes(x=dates,y=fraction,color=state))+geom_pointrange(data=all_filtered,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
theme(legend.position = "bottom")+
theme(panel.background = element_blank())+
theme(text = element_text(family = "Times New Roman"),axis.text.x = element_text(angle = 70, hjust = 1))+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(x="Survey Dates",color="State")+
scale_x_date( breaks=all_filtered$dates,date_labels="%b %d %y")
Scale for 'colour' is already present. Adding another scale for 'colour', which
will replace the existing scale.
This is just the susceptible species disease incidence, sctld prevalence (fraction of cols with SCTLD signs), and dead colonies.
#stats_filtered is from above, where I used the aggregate function to get mean and SE
stats_shortfiltered<-subset(stats_filtered,subset=dates>="2018-10-30")
incidence_long$site<-c(rep(3,42),rep(1,42),rep(2,42))
sites.incidence<-aggregate(newI~site+date,FUN=sum,data=incidence_long)
sites.incidence
site date newI
1 1 2018-10-30 0
2 2 2018-10-30 1
3 3 2018-10-30 0
4 1 2018-11-09 0
5 2 2018-11-09 1
6 3 2018-11-09 0
7 1 2018-11-29 1
8 2 2018-11-29 1
9 3 2018-11-29 0
10 1 2018-12-13 0
11 2 2018-12-13 2
12 3 2018-12-13 0
13 1 2019-01-04 4
14 2 2019-01-04 4
15 3 2019-01-04 0
16 1 2019-01-18 1
17 2 2019-01-18 6
18 3 2019-01-18 0
19 1 2019-02-08 1
20 2 2019-02-08 1
21 3 2019-02-08 2
22 1 2019-03-04 5
23 2 2019-03-04 1
24 3 2019-03-04 13
25 1 2019-03-21 3
26 2 2019-03-21 1
27 3 2019-03-21 10
28 1 2019-04-11 3
29 2 2019-04-11 0
30 3 2019-04-11 29
31 1 2019-05-02 3
32 2 2019-05-02 0
33 3 2019-05-02 16
34 1 2019-05-16 2
35 2 2019-05-16 0
36 3 2019-05-16 25
37 1 2019-05-28 1
38 2 2019-05-28 2
39 3 2019-05-28 5
40 1 2019-06-13 1
41 2 2019-06-13 0
42 3 2019-06-13 6
43 1 2019-07-01 1
44 2 2019-07-01 0
45 3 2019-07-01 2
46 1 2019-07-22 0
47 2 2019-07-22 1
48 3 2019-07-22 0
49 1 2019-08-16 0
50 2 2019-08-16 0
51 3 2019-08-16 1
52 1 2019-09-17 0
53 2 2019-09-17 0
54 3 2019-09-17 1
55 1 2019-10-14 0
56 2 2019-10-14 1
57 3 2019-10-14 2
58 1 2019-11-12 0
59 2 2019-11-12 1
60 3 2019-11-12 11
61 1 2019-12-06 2
62 2 2019-12-06 0
63 3 2019-12-06 7
str(stats_shortfiltered)
'data.frame': 126 obs. of 5 variables:
$ state : Factor w/ 4 levels "Healthy","SCTLD",..: 2 3 2 3 2 3 2 3 2 3 ...
$ dates : Date, format: "2018-10-30" "2018-10-30" ...
$ Site : Factor w/ 3 levels "3","1","2": 2 2 2 2 2 2 2 2 2 2 ...
$ fraction: num 0 0.00237 0 0.00237 0.00167 ...
$ stander : num 0 0.00237 0 0.00237 0.00167 ...
sites.incidence$newInc.scaled<-sites.incidence$newI/100
prev.data<-subset(stats_shortfiltered,subset=Site=="1")
inc.data<-subset(sites.incidence,subset=site=="1")
inc.data<-subset(inc.data,subset=date>="2018-10-30")
class(inc.data$date)
[1] "Date"
prev.data
state dates Site fraction stander
11 SCTLD 2018-10-30 1 0.000000000 0.0000000000
12 Dead 2018-10-30 1 0.002369668 0.0023696682
13 SCTLD 2018-11-09 1 0.000000000 0.0000000000
14 Dead 2018-11-09 1 0.002369668 0.0023696682
15 SCTLD 2018-11-29 1 0.001666667 0.0016666667
16 Dead 2018-11-29 1 0.002369668 0.0023696682
17 SCTLD 2018-12-13 1 0.001666667 0.0016666667
18 Dead 2018-12-13 1 0.002369668 0.0023696682
19 SCTLD 2019-01-04 1 0.011145340 0.0078120063
20 Dead 2019-01-04 1 0.002369668 0.0023696682
21 SCTLD 2019-01-18 1 0.012812006 0.0061453397
22 Dead 2019-01-18 1 0.002369668 0.0023696682
23 SCTLD 2019-02-08 1 0.015181675 0.0085150079
24 Dead 2019-02-08 1 0.002369668 0.0023696682
25 SCTLD 2019-03-04 1 0.019218009 0.0092180095
26 Dead 2019-03-04 1 0.008072670 0.0014060032
27 SCTLD 2019-03-21 1 0.012109005 0.0021090047
28 Dead 2019-03-21 1 0.020181675 0.0035150079
29 SCTLD 2019-04-11 1 0.016145340 0.0028120063
30 Dead 2019-04-11 1 0.021848341 0.0018483412
31 SCTLD 2019-05-02 1 0.012109005 0.0021090047
32 Dead 2019-05-02 1 0.031587678 0.0015876777
33 SCTLD 2019-05-16 1 0.012812006 0.0061453397
34 Dead 2019-05-16 1 0.042290679 0.0043759874
35 SCTLD 2019-05-28 1 0.010442338 0.0037756714
36 Dead 2019-05-28 1 0.047030016 0.0003633491
37 SCTLD 2019-06-13 1 0.008072670 0.0014060032
38 Dead 2019-06-13 1 0.051769352 0.0051026856
39 SCTLD 2019-07-01 1 0.007369668 0.0026303318
40 Dead 2019-07-01 1 0.054139021 0.0074723539
41 SCTLD 2019-07-22 1 0.007369668 0.0026303318
42 Dead 2019-07-22 1 0.054139021 0.0074723539
43 SCTLD 2019-08-16 1 0.007369668 0.0026303318
44 Dead 2019-08-16 1 0.054139021 0.0074723539
45 SCTLD 2019-09-17 1 0.005703002 0.0009636651
46 Dead 2019-09-17 1 0.055805687 0.0058056872
47 SCTLD 2019-10-14 1 0.001666667 0.0016666667
48 Dead 2019-10-14 1 0.058175355 0.0081753555
49 SCTLD 2019-11-12 1 0.001666667 0.0016666667
50 Dead 2019-11-12 1 0.062211690 0.0088783570
51 SCTLD 2019-12-06 1 0.005703002 0.0009636651
52 Dead 2019-12-06 1 0.072211690 0.0011216430
s1.ts<-ggplot()+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
geom_bar(data=inc.data,aes(x=date,y=newInc.scaled),stat="identity",fill="light grey")+
scale_y_continuous(name = "Fraction of colonies", breaks=seq(0,.1,.01),expand = c(0, 0),limits=c(0,.1),sec.axis = sec_axis(~.*100,name ="Disease incidence",breaks=c(0,2,4,6,8,10)))+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
theme(legend.position = "none")+
theme(panel.background = element_blank())+
theme(text = element_text(family = "Times New Roman"),axis.text.x =element_blank())+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(x="",color="State")+
scale_x_date( breaks=stats_filtered$dates,date_labels="")
Scale for 'colour' is already present. Adding another scale for 'colour', which
will replace the existing scale.
s1.ts
prev.data<-subset(stats_shortfiltered,subset=Site=="2")
inc.data<-subset(sites.incidence,subset=site=="2")
inc.data<-subset(inc.data,subset=date>="2018-10-30")
prev.data
state dates Site fraction stander
63 SCTLD 2018-10-30 2 0.002645503 0.0026455026
64 Dead 2018-10-30 2 0.000000000 0.0000000000
65 SCTLD 2018-11-09 2 0.002392344 0.0023923445
66 Dead 2018-11-09 2 0.002645503 0.0026455026
67 SCTLD 2018-11-29 2 0.004784689 0.0047846890
68 Dead 2018-11-29 2 0.002645503 0.0026455026
69 SCTLD 2018-12-13 2 0.010075694 0.0005063163
70 Dead 2018-12-13 2 0.002645503 0.0026455026
71 SCTLD 2019-01-04 2 0.017505886 0.0016328701
72 Dead 2019-01-04 2 0.005037847 0.0002531581
73 SCTLD 2019-01-18 2 0.027075264 0.0112022480
74 Dead 2019-01-18 2 0.017505886 0.0016328701
75 SCTLD 2019-02-08 2 0.022290575 0.0064175590
76 Dead 2019-02-08 2 0.024682919 0.0088099035
77 SCTLD 2019-03-04 2 0.009569378 0.0095693780
78 Dead 2019-03-04 2 0.039796461 0.0080504291
79 SCTLD 2019-03-21 2 0.011961722 0.0119617225
80 Dead 2019-03-21 2 0.042188805 0.0104427736
81 SCTLD 2019-04-11 2 0.007177033 0.0071770335
82 Dead 2019-04-11 2 0.046973494 0.0152274626
83 SCTLD 2019-05-02 2 0.004784689 0.0047846890
84 Dead 2019-05-02 2 0.054656844 0.0123288018
85 SCTLD 2019-05-16 2 0.004784689 0.0047846890
86 Dead 2019-05-16 2 0.054656844 0.0123288018
87 SCTLD 2019-05-28 2 0.007177033 0.0071770335
88 Dead 2019-05-28 2 0.054656844 0.0123288018
89 SCTLD 2019-06-13 2 0.004784689 0.0047846890
90 Dead 2019-06-13 2 0.057049189 0.0147211463
91 SCTLD 2019-07-01 2 0.004784689 0.0047846890
92 Dead 2019-07-01 2 0.057049189 0.0147211463
93 SCTLD 2019-07-22 2 0.007430192 0.0021391863
94 Dead 2019-07-22 2 0.057049189 0.0147211463
95 SCTLD 2019-08-16 2 0.007430192 0.0021391863
96 Dead 2019-08-16 2 0.059694691 0.0120756437
97 SCTLD 2019-09-17 2 0.004784689 0.0047846890
98 Dead 2019-09-17 2 0.072415888 0.0089238247
99 SCTLD 2019-10-14 2 0.007430192 0.0021391863
100 Dead 2019-10-14 2 0.075061391 0.0062783221
101 SCTLD 2019-11-12 2 0.009822536 0.0045315308
102 Dead 2019-11-12 2 0.075061391 0.0062783221
103 SCTLD 2019-12-06 2 0.009822536 0.0045315308
104 Dead 2019-12-06 2 0.087529430 0.0081643503
s2.ts<-ggplot()+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
geom_bar(data=inc.data,aes(x=date,y=newInc.scaled),stat="identity",fill="light grey")+
scale_y_continuous(name = "Fraction of colonies", breaks=seq(0,.1,.01),expand = c(0, 0),limits=c(0,.1),sec.axis = sec_axis(~.*100,name ="Disease incidence",breaks=c(0,2,4,6,8,10)))+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
theme(legend.position = "none")+
theme(panel.background = element_blank())+
theme(text = element_text(family = "Times New Roman"),axis.text.x = element_blank())+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(x="",color="State")+
scale_x_date( breaks=stats_filtered$dates,date_labels="%b %d %y")
Scale for 'colour' is already present. Adding another scale for 'colour', which
will replace the existing scale.
s2.ts
prev.data<-subset(stats_shortfiltered,subset=Site=="3")
inc.data<-subset(sites.incidence,subset=site=="3")
inc.data<-subset(inc.data,subset=date>="2018-10-30")
s3.ts<-ggplot()+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
geom_bar(data=inc.data,aes(x=date,y=newInc.scaled),stat="identity",fill="light grey")+
scale_y_continuous(name = "Fraction of colonies", breaks=seq(0,.3,.05),expand = c(0, 0),limits=c(0,.30),sec.axis = sec_axis(~.*100,name ="Disease incidence",breaks=c(0,5,10,15,20,25,30)))+
geom_line(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+
geom_point(data=prev.data,mapping=aes(x=dates,y=fraction,color=state))+geom_pointrange(data=prev.data,aes(x=dates,y=fraction,color=state,ymin=fraction-stander, ymax=fraction+stander))+
scale_colour_manual(values=c("Healthy"="blue","SCTLD"="red","Dead"="black","Unknown"="grey"),drop=TRUE)+
theme(legend.position = "bottom")+
theme(panel.background = element_blank())+
theme(text = element_text(family = "Times New Roman"),axis.text.x =element_blank())+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(), axis.line = element_line(colour = "black"))+
labs(x="",color="State")+
scale_x_date( breaks=stats_filtered$dates,date_labels="")+
theme(text = element_text(family = "Times New Roman"),axis.text.x = element_text(angle = 70, hjust = 1))+
scale_x_date( breaks=stats_filtered$dates,date_labels="%d %b %y")
Scale for 'colour' is already present. Adding another scale for 'colour', which
will replace the existing scale.
Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
s3.ts
tiff("Figure2.tiff",width=180, height=300,units="mm",res=300)
(s2.ts/s1.ts/s3.ts) + plot_annotation(tag_levels="A")
dev.off()
null device
1